Inferensys

Glossary

AutomationML

An open, XML-based data exchange format for storing and transferring engineering data between heterogeneous software tools in the manufacturing automation domain.
Developer demonstrating multi-agent tool use, agent tool selection interface on laptop, casual tech demo moment.
ENGINEERING DATA EXCHANGE

What is AutomationML?

AutomationML (Automation Markup Language) is an open, XML-based data exchange format designed to store and transfer heterogeneous engineering data between disparate software tools throughout the manufacturing automation lifecycle.

AutomationML is a neutral, open-standard data format based on XML that interconnects engineering information from mechanical design, electrical planning, and control logic tools. It achieves this by combining established standards: CAEX for hierarchical plant topology, COLLADA for 3D geometry and kinematics, and PLCopen XML for logic and behavior sequences.

By linking these domain-specific models through a single top-level structure, AutomationML enables lossless data exchange without proprietary interfaces. This semantic interoperability is foundational for digital twin engineering, allowing a unified virtual representation of a production system to be built and shared across the entire value chain.

ENGINEERING DATA EXCHANGE

Key Features of AutomationML

AutomationML (Automation Markup Language) is an open, XML-based standard (IEC 62714) designed to represent and exchange engineering data across heterogeneous tools. It bridges mechanical, electrical, and control engineering domains by interlinking existing formats into a single, lossless data model.

01

Top-Level CAEX Architecture

Uses CAEX (IEC 62424) as the structural backbone to model the plant topology. This object-oriented data model represents the hierarchical structure of a manufacturing system—from the enterprise level down to individual sensors and actuators. CAEX defines System Unit Classes (templates) and Internal Elements (instances), establishing the semantic relationships and nesting of all physical and logical components.

IEC 62424
Base Standard
02

Geometry with COLLADA

Stores detailed 3D kinematic and visual geometry using COLLADA (ISO/PAS 17506). Unlike lightweight formats, COLLADA preserves the full kinematic chain, including joint axes, articulation limits, and mesh data. AutomationML links each geometry instance to its corresponding CAEX structural element, ensuring the visual representation is semantically tied to the plant hierarchy for accurate virtual commissioning and collision detection.

ISO/PAS 17506
Geometry Standard
03

Logic & Sequencing via PLCopen XML

Represents control behavior and sequential logic using PLCopen XML (IEC 61131-10). This captures the full IEC 61131-3 programming model—including Ladder Diagram, Structured Text, and Sequential Function Chart—in an open XML schema. AutomationML links each Program Organization Unit (POU) to the specific CAEX element it controls, creating a direct, traceable relationship between software logic and physical equipment.

IEC 61131-10
Logic Standard
04

Role Class Libraries

Defines reusable, vendor-neutral Role Classes that abstract the function of a component from its specific implementation. For example, a 'Motor' role defines the generic interface and behavior expected of any motor, while a specific manufacturer's part implements that role. This semantic abstraction layer enables multi-vendor engineering and simplifies the replacement of physical devices without rewriting the overarching control logic or plant model.

Vendor-Neutral
Abstraction Layer
05

Flexible Interlinking Model

Employs a non-intrusive linking mechanism that references external documents rather than embedding them. CAEX elements contain ExternalReference nodes that point to COLLADA geometry files, PLCopen XML logic files, or any other engineering document via URI. This preserves the integrity of native tool formats, avoids data duplication, and allows domain experts to work in their preferred authoring tools while maintaining a single, coherent data backbone.

URI-Based
Linking Method
06

Standardized Interface Libraries

Provides AutomationML Interface Libraries that standardize the ports, signals, and data flow connections between components. These libraries define physical interfaces (e.g., electrical terminals, pneumatic ports) and logical interfaces (e.g., OPC UA variables, network signals). By formalizing how components connect and communicate, AutomationML enables automated consistency checks and seamless integration with Asset Administration Shells (AAS) for Industry 4.0 deployments.

AAS Compatible
Industry 4.0
AUTOMATIONML CLARIFIED

Frequently Asked Questions

Clear, technically precise answers to the most common questions about the AutomationML data exchange standard for manufacturing engineering.

AutomationML (Automation Markup Language) is an open, XML-based data exchange format designed to store and transfer engineering data between heterogeneous software tools in the manufacturing automation domain. It works by combining three established standards: CAEX (IEC 62424) for representing the hierarchical plant topology, COLLADA (ISO/PAS 17506) for storing geometry and kinematics, and PLCopen XML for representing logic and behavior. This neutral, tool-independent format enables seamless interoperability across the entire engineering lifecycle, from mechanical design and electrical planning to control programming. AutomationML does not define a new data model but rather integrates these existing formats under a single container architecture, allowing engineers to exchange complete mechatronic project data without proprietary lock-in.

Prasad Kumkar

About the author

Prasad Kumkar

CEO & MD, Inference Systems

Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.

His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.